MLOps Foundations: Designing and Deploying Production ML Pipelines

Moving a machine learning model from a Jupyter notebook to a reliable, automated production environment is one of the most critical challenges in modern engineering. Many developers struggle with the complexities of versioning data, automating deployments, and monitoring model degradation in real time. This text-based course guides you through the entire lifecycle of MLOps, transforming you from a model builder into a production-ready engineer. You will understand how to design robust pipelines that automatically train, validate, deploy, and monitor machine learning models with confidence. What you'll learn: - Understand foundational MLOps terminology, core pipeline architectures, and the transition from research to production. - Implement data and model versioning using industry-standard tools like DVC and MLflow. - Build automated CI/CD workflows with GitHub Actions to test and deploy models safely. - Configure real-time monitoring and alerting systems to detect model drift and performance degradation. - Deploy scalable ML services using API frameworks, containerization, and basic orchestration concepts. - Apply A/B testing methodologies and guardrails to evaluate model updates in live environments. The course starts with essential terminology and structural concepts before guiding you through step-by-step written explanations of CI/CD integration, model tracking, and deployment strategies. You will read real-world scenarios and practice your skills through structured conceptual exercises. This course is designed for aspiring ML engineers, data scientists, and software developers who are new to MLOps. No prior DevOps experience is required, as we begin with the absolute basics of production systems. Start reading today to bridge the gap between local data science and scalable production engineering.